Object trajectory simulation

ABSTRACT

A method may include receiving a group of images taken by a camera over time in an environment, in which the camera may be oriented within the environment to capture images of an object in a substantially same direction as a launch direction of the object, and the group of images including a first image and a second image. The method may further include: identifying a first position of the object in the first image; identifying a second position of the object in the second image; generating a flight vector based on the first position of the object and the second position of the object; and determining one or more flight parameters using the flight vector. Additionally, the method may include: generating a simulated trajectory of the object based on the flight parameters; and providing the simulated trajectory of the object for presentation in a graphical user interface.

FIELD

The application relates generally to object trajectory simulation.

BACKGROUND

Objects sometimes may travel through the air. It may be difficult tosimulate how the object travels through the air and thus a landingposition of the object, particularly in situations where one or moreflight parameters of the object may be difficult to ascertain. Somecomputer systems may attempt to determine one or more flight parametersof an object but lead to widely inaccurate flight simulations of theobject due to limited and/or erroneous flight parameter inputs for somecomputer systems.

The subject matter claimed herein is not limited to embodiments thatsolve any disadvantages or that operate only in environments such asthose described above. Rather, this background is only provided toillustrate one example technology area where some embodiments describedherein may be practiced.

SUMMARY

Embodiments of the disclosure discuss various operations performed in amethod, system, and/or computer-readable medium. The operations mayinclude receiving a plurality of images taken by a camera over time inan environment, the camera oriented within the environment to captureimages of an object in a substantially same direction as a launchdirection of the object, the plurality of images including a first imageand a second image. The operations may further include: identifying afirst position of the object in the first image; identifying a secondposition of the objection in the second image; generating a flightvector based on respective positions of the object in the first andsecond images; and determining one or more flight parameters using theflight vector. Additionally, the operations may include: generating asimulated trajectory of the object based on the flight parameters; andproviding the simulated trajectory of the object for presentation in agraphical user interface.

BRIEF DESCRIPTION OF THE DRAWINGS

Example embodiments will be described and explained with additionalspecificity and detail through the use of the accompanying drawings inwhich:

FIG. 1 illustrates an example environment to simulate a trajectory of anobject;

FIGS. 2A-2B respectively illustrate an example environment captured atdifferent times in a first image and a second image;

FIG. 2C illustrates an example relationship between observed positionsof the object in the environment of FIGS. 2A-2B;

FIG. 3 illustrates an example block diagram having example inputs andoutputs related to a launched object;

FIG. 4 illustrates an example method to simulate a trajectory of anobject; and

FIG. 5 illustrates a block diagram of an example computing system.

DESCRIPTION OF EMBODIMENTS

Many technologies and fields may benefit from imaging analysis. Forexample, in sports, imaging analysis may help identify whether: a tennisserve was on the line, a foot was inbounds, a ball touched the ground,illegal contact was made, a baseball strike actually crossed home plate,etc. Such examples of imaging analysis may be used in review processesof events after they occur, now common in a variety of sports.Additionally, imaging analysis and/or other sensor detection may be usedin real-time processes. For example, high speed cameras may illustrateas an event occurs and sometimes in seemingly slow-motion: a balloonpopping, lightning striking, or an explosion detonating. In otherscenarios, radar sensors may be used to determine a speed of an objectas an event occurs, e.g., a baseball pitch, a tennis serve, a tee-shot,etc. Additionally, imaging analysis and/or other sensor detection may beused in predictive processes. Such predictive processes may be difficultand/or computationally intense because in addition to accuratelygathering real-time data, simulated data may be generated based on thereal-time data. Thus, in some conventional methods and systems,predicting or simulating a flight trajectory of an object may be undulylimited or unreliable. Smartphone hardware, low-cost hardware, and/orslow frame rate speeds of some cameras may further exacerbate theexample problematic limitations and reliability of other methods andsystems.

For example, some other methods and systems may endeavor to simulate aflight trajectory of an object. However, these other methods and systemsmay extrapolate flight parameters based on captured images and comparethe extrapolated flight parameters with sensor data, such as radar data.The sensor data may then be used to modify the extrapolated flightparameters to account for error and/or poor image quality. Problems withthese other systems may include: 1) extrapolation error; 2)over-reliance on radar data to remedy extrapolation error; and 3)over-reliance on high resolution and/or high frame rate imaging. Myriadother drawbacks with conventional systems exist.

Aspects of the present disclosure address these and other problems withconventional methods and systems by providing a new, software-basedapproach that improves accuracy of the simulated trajectory andassociated flight parameters while also allowing for system flexibilityin a variety of devices, such as a client device, a measuring device, orother suitable device.

Thus, according to one or more embodiments of the present disclosure, adevice may be placed behind a player to acquire imaging data and deduceflight parameters of a launched object such as speed, spin axis, rate ofspin, launch angle, launch direction, etc. Additionally oralternatively, radar data may be used to deduce one or more of theflight parameters. Additionally or alternatively, markings on the objectand associated rotational blur may be used to deduce one or more of theflight parameters. Additionally or alternatively, a rolling shutter of acamera may be used to deduce one or more of the flight parameters. Inthese or other embodiments, the flight parameters may be input into aflight model to determine a simulated a trajectory of the object.Additionally or alternatively, one or more of the flight parameters maybe determined as the simulated trajectory is modeled.

In some embodiments of the present disclosure, club identification maybe employed. For example, using image processing and image recognitiontechniques, a club may be identified and/or presented at a display. Theclub may be used to launch the object. In these or other embodiments ofthe present disclosure, flight mapping data may be generated and/orpresented at a display. The flight mapping data may include, relative toan environment for launching the object, one or more of globalpositioning system (GPS) coordinates, compass data, accelerometer data,target data, and satellite or flyby images. Additionally oralternatively, the simulated trajectory may be combined with one or moreaspects of the flight mapping data, e.g., for presentation at a display.In these or other embodiments of the present disclosure, the display maybe a display of a client device and/or a display of another device, suchas a TV, monitor, or other suitable display. Such displays may benetwork-connected displays and may be used in location-specificentertainment, competitions, etc.

FIG. 1 illustrates an example environment 100 to simulate a trajectory130 of an object 125. The environment 100 is arranged in accordance withat least one embodiment of the present disclosure. As illustrated, theenvironment 100 may include a data manager 105 having a computing system110 and a neural network 115, sensors 120 having a field of view 122,the object 125, the trajectory 130, a starting point 135, a targetregion 137, a player 140, and a club 145.

In some embodiments, the data manager 105 may direct operations of thecomputing system 110 and/or the neural network 115. Additionally oralternatively, the data manager 105 may facilitate communication betweenthe system 110 and the neural network 115. Additionally oralternatively, the data manager 105 may facilitate communication betweenthe sensors 120 and any of the computing system 110 and the neuralnetwork 115.

In some embodiments, the data manager 105 may be part of a clientdevice. Some examples of the client device may include a mobile phone, asmartphone, a tablet computer, a laptop computer, a desktop computer, aset-top box, a virtual-reality device, an augmented reality device, awearable device, a connected device, a measurement device, etc.

In some embodiments, the computing system 110 may include any computersystem, such as the system 500 described in conjunction with FIG. 5 . Inthese or other embodiments, the neural network 115 may include anylearning-based mechanism. Examples of neural networks may include:perceptron, multilayer peceptron, feed forward, radial basis network,deep feed forward, recurrent neural network, long/short term memory,gated recurrent unit, auto encoder, variational auto encoder, denoisingauto encoder, sparse auto encoder, any sequence-to-sequence model,shallow neural networks, markov chain, hopfield network, boltzmannmachine, restricted boltzmann machine, deep belief network, deepconvolutional network, convolutional neural network (e.g., VGG-16),deconvolutional network, deep convolutional inverse graphics network,modular neural network, generative adversarial network, liquid statemachine, extreme learning machine, echo state network, recursive neuralnetwork, deep residual network, kohonen network, support vector machine,neural turing machine, etc.

The neural network 115 may receive data from one or more of the sensors120. Any of the sensors 120 may be included as part of the client deviceor may be a device separate from the client device. In some embodiments,the field of view 122 may be a three-dimensional space in which thesensors 120 may image (e.g., in video mode or picture mode), senseindications of events, and/or obtain data. The sensors 120 may bepositioned such that the field of view 122 includes the starting point135 of the trajectory 130 and/or a portion of the environment 100 beyondthe starting point 135. In these or other embodiments, the sensors 120may be positioned such that the field of view 122 is aligned orapproximately aligned with the starting point 135 and the target region137.

The neural network 115 may use the data generated by one or more of thesensors 120 to learn during a training process (e.g., to populate one ormore layers or neurons in the neural network 115). Additionally oralternatively, the neural network 115 may use the data generated by oneor more of the sensors 120 to learn post-training (e.g., to re-populateone or more layers or neurons in the neural network 115 or to populatelayers or neurons in response to changed circumstances in theenvironment 100).

For example, using data generated by one or more of the sensors 120, theneural network 115 may learn to identify the club 145 (e.g., afterseveral, tens, hundreds, thousands, or millions of training shots).Training the neural network 115 to automatically identify the club 145may include receiving data generated by one or more of the sensors 120in which the data may correspond to images of the club 145 and/or otherclubs of a variety of club types. Examples of different club types mayinclude woods, irons, and hybrids. Additionally or alternatively, clubsof all types may include a club identifier such as a number or name, ashape, an outline, a material, an amount of reflectivity, a shaft, aface angle, a head measurement, etc. that may help to identify the club145. As the neural network 115 receives more and more data correspondingto different clubs, the neural network 115 may increase in accuracy withrespect to identification of the club 145. Additionally oralternatively, the neural network 115 may learn a repertoire of clubsthat are specific to the player 140 (e.g., as available to the player140, purchased by the player 140, and/or previously used by the player140). Other examples including the neural network 115 are described withadditional specificity in conjunction with FIG. 3 .

In some embodiments, the sensors 120 may include: a camera: a radarsensor, an accelerometer, a gyroscope, or any other suitable sensorconfigured to sense changes in light, pixels, sound, motion, rotation,position, orientation, magnetization, acceleration, tilt, vibration,force, speed, color, wind, etc.

In some embodiments, the object 125 may include any projectile, forexample, a golf ball, a baseball, a tennis ball, a kickball, a cricketball, a shuttlecock, etc. Accordingly, the scope of the presentdisclosure may include a wide variety of sports and activities in whichit may be desirable to simulate the trajectory 130 of an object 125.Although some of the figures and corresponding description may beassociated with one or more example sports or activities, the scope ofthe present disclosure is not limited to those example sports oractivities given the variety of applications herein contemplated.

In some embodiments, the trajectory 130 may include a path of travel ofthe object 125. In these or other embodiments, the sensors 120 maygenerate data that depicts the object 125 at various positions and/ororientations. Based on the generated data from the sensors 120 depictingthe object 125 at various positions and/or orientations, the datamanager 105 may determine one or more flight parameters of the object125 to simulate the trajectory 130. Determining the flight parametersand trajectory 130 of the object 125 is discussed in greater detail inconjunction with FIGS. 2A, 2B, and 2C along with FIG. 3 .

As referred to in the present disclosure, the starting point 135 mayinclude a position of the object 125 at which there is detected abeginning of motion of the object 125 approximately along the trajectory130. For example, the starting point 135 may include a position at whichthe object 125 is detected as having left a tee box, a launch area, or astarting area. Additionally or alternatively, the starting point 135 mayinclude a position at which the object 125 is detected as being on theground (e.g., on a playing surface, a tee, a court, a field, etc.). Inthese or other embodiments, the starting point 135 may include aposition of the object 125 when stationary prior to a force beingexerted against the object 125 to impart motion of the object 125 in adirection approximately along the trajectory 130. Thus, in someembodiments, the starting point 135 of the trajectory 130 may includethe position of the object 125 at some point prior to, during, or afterthe object 125 is shot along the trajectory 130. Additionally oralternatively, the starting point 135 may correspond to globalpositioning system (GPS) coordinates, e.g., of a client device, ameasurement device, a camera of the sensors 120, or any other suitabledevice configured to determine GPS coordinates and that may also be inproximity to the object 125 when stationary prior to launch. Forexample, a client device associated with the player 140 may, in turn, bepositionally associated with the object 125 at time of impact forlaunching the object 125 along the trajectory 130.

In some embodiments, the target region 137 may correspond to a locationon the ground (e.g., a court, a field, a playing surface, etc.). Forexample, the target region 137 may include an estimated area (e.g.,circular, rectangular, or triangular area having some dimensionalboundaries such as about two feet by two feet). In these or otherembodiments, the target region 137 may include the estimated area atwhich the player 140 intends to place the object 125, e.g., a fairway, abunker, a green, a fringe, a flag, a cup, a target (e.g., a coloredtarget like in TopGolf® games), etc. Additionally or alternatively, thetarget region 137 may include an estimated point (e.g., a dot, an “X”,or other symbol) that indicates a GPS coordinate on the ground. Forexample, the target region 137 may include an estimated GPS coordinateor pin-point location on the ground that corresponds to where the player140 intends to place the object 125. Additionally or alternatively, thetarget region 137 may be selectable. For example, the player 140 maychoose to select the target region 137 via the computing system 110. Inanother example, the target region 137 may be inferred based on thetrajectory 130, e.g., via the computing system 110. In some embodiments,the object 125 need not arrive to or be placed at the target region 137.For example, the player 140 may intend to place the object 125, but apoor shot may put the object 125 at a different position than the targetregion 137. Thus, in some embodiments, the trajectory 130 may not end atthe target region 137 in some cases, while in other cases, thetrajectory 130 may end at the target region 137.

Modifications, additions, or omissions may be made to the environment100 without departing from the scope of the present disclosure. Forexample, in some embodiments, the environment 100 may include any numberof other components that may not be explicitly illustrated or described.

Moreover, the separation of various components in the embodimentsdescribed herein is not meant to indicate that the separation occurs inall embodiments. In addition, it may be understood with the benefit ofthis disclosure that the described components may be integrated togetherin a single component or separated into multiple components.

FIGS. 2A-2B respectively illustrate an example environment 200 capturedin a first image 202 at time t₁ and a second image 204 at a differenttime t2. The environment 200 may be arranged in accordance with at leastone embodiment described in the present disclosure. As illustrated inthe first image 202, the player 140, the club 145, a first scan line205, a second scan line 210, an object 225, and a starting point 235 aredepicted with respect to the environment 200. The object 225 and thestarting point 235 may be the same as or similar to the object 125 andthe starting point 135, respectively, of FIG. 1 .

In these or other embodiments, a radar sensor or a motion sensor maydetect that the object 225 has been launched due to motion of the object225 and/or detect that the object 225 will be launched due to motion ofthe club 145. Based on the detected motion, a computing system (e.g.,the computing system 110 of FIG. 1 ) communicatively coupled to theradar sensor or motion sensor may trigger an identification process ofthe object 225 in one or more images taken by a camera.

In some embodiments, identification of the object 225 may includeidentifying pixels that represent at least a portion of the object 225.This may be done in myriad different ways. For example, in one exampleway using background subtraction methods, pixels representing at least aportion of the object 225 may be searched for and/or identified (e.g.,by the data manager 105 of FIG. 1 ). Background subtraction methods mayinclude any set of techniques that detect changes in data (e.g., scenechanges due to changes in pixilation) in reference to a pre-processedimage. Some example background subtraction techniques may include atemporal average filter algorithm, a frame differencing algorithm, amean filter algorithm, a running Gaussian average algorithm, abackground mixture algorithm, etc., and any combination thereof. Sampleequations and examples of some types of background subtraction methodsare discussed in further detail in the paper entitled A COMPREHENSIVEREVIEW OF BACKGROUND SUBTRACTION ALGORITHMS EVALUATED WITH SYNTHETIC ANDREAL VIDEOS by Andrews Sobral and Antoine Vacavant of Computer Visionand Image Understanding, Volume 122, May 2014, pages 4-21 (hereafter theSobral Paper). The contents of the Sobral Paper in its entirety areexpressly incorporated by reference into the present disclosure.

Additionally or alternatively, in another example way, pixelsrepresenting at least a portion of the object 225 may be searched forand/or identified using a K-nearest neighborhood algorithm. In these orother embodiments, the neural network such as the neural network 115 ofFIG. 1 may be implemented in identifying pixels representing at least aportion of the object 225. Sample equations and examples of some typesof K-nearest neighborhood algorithms are discussed in further detail inthe paper entitled K-Nearest Neighbor Search for Moving Query Point byZhexuan Song and Nick Roussopoulos as presented in Advances in Spatialand Temporal Databases in the International Symposium on Spatial andTemporal Databases 2001 (hereafter the Song Paper). The contents of theSong Paper in its entirety are expressly incorporated by reference intothe present disclosure.

In some embodiments, the identified pixels representing at least aportion of the object 225 may represent a particular aspect or featureof the object 225. For example, the identified pixels may representmarkings, shapes, numbers, patterns, a center of gravity, a mid-point, acenter line, a stitching, a seam, an air inlet, a letter, etc. of theobject 225. In these or other embodiments, the identified pixelsrepresentative of a particular aspect or feature of the object 225 may,in turn, be used to identify a position of the object 225 in arespective image. For example, a pixel or group of pixels may correspondto a spin axis of the object 225. Additionally or alternatively, theposition (e.g., in the U-V coordinate system) of the spin axis of theobject 225 may be designated as the position of the object 225 as awhole. In this manner, a position of the object 225 in one or moreimages generated by the camera of the sensors 220 may be identified.

In some embodiments, the first image 202 and the second image 204 may becaptured via a rolling shutter method. In the rolling shutter method,the environment 200 may not all be captured at a single instant, but mayinstead be captured portion by portion in chronological sequence, e.g.,according to vertical or horizontal segments such as scan lines. Inthese or other embodiments, the rolling shutter method may help to moreaccurately determine a time difference between the object 225 positionedat (U₁, V₁) in the first image 202 and the object 225 positioned at (U₂,V₂) in the second image 204 in a U-V coordinate system from aperspective of a camera. For example, in a 1080p image (e.g., 1,920horizontal pixel lines and 1,080 vertical pixel lines), a time for eachscan line in a roller shutter image may be approximately 30 microsecondsfor a frame rate of 30 fps. The time for each scan line may bedetermined by dividing the frame rate inverse (e.g., 1s/30frames) by theapproximate number of scan lines (e.g., about 1080, about 1100, or someother suitable number of scan lines) in an image. Accordingly, theobject 225 at (U₁, V₁) in the first image 202 may be associated with thefirst scan line 205, and the object 225 positioned at (U₂, V₂) in thesecond image 204 may correspond with the second scan line 210.

Additionally or alternatively, the first scan line 205 and the secondscan line 210 may be known, (e.g., the 565^(th) scan line for the firstscan line 205 and the 623^(rd) scan line for the second scan line 210).Thus, in some embodiments, a time difference between the object 225positioned at (U₁, V₁) in the first image 202 and the object 225positioned at (U₂, V₂) in the second image 204 may be determined asfollows: a time for each scan line multiplied by the number of scanlines between the object 225 positioned at (U₁, V₁) in the first image202 and the object 225 positioned at (U₂, V₂) in the second image 204.Where, for example, the first image 202 and the second image 204 areconsecutive images, the time difference between (U₁, V₁) and (U₂, V₂) inthe respective images may be (using the above example numbers)determined according to the expression: 30 μs*((1080-565)+623), wherethe term “30 μs” is the example approximate time for each scan line, theterm (1080-565) is the example number of approximate scan linesremaining in the first image 202 after (U₁, V₁), and the term “623” isthe example number of approximate scan lines in the second image 204 toreach the position (U₂, V₂). In this example scenario, the timedifference between the object 225 positioned at (U₁, V₁) in the firstimage 202 and the object 225 positioned at (U₂, V₂) in the second image204 equates to approximately 34 ms. The time difference may be one ofthe flight parameters 325 input into the data manager 305 to determineone or more aspects of shot data 340 described in conjunction with FIG.3 .

Additionally or alternatively, the time difference between the object225 positioned at (U₁, V₁) in the first image 202 and the object 225positioned at (U₂, V₂) in the second image 204 (hereafter “t₁₂”) may beused to determine a time of impact to the object 225. In these or otherembodiments, the time of impact to the object 225 may be another one ofthe flight parameters 325 input into the data manager 305 to determineone or more aspects of shot data 340 described in conjunction with FIG.3 . In some embodiments, accurately determining the time of impact tothe object 225 may, using other methods and systems, be difficult toascertain because the impact to the object 225 may occur between imageframes. However, aspects of the present disclosure address this exampledifficulty using a cross ratio of perspective projection. The crossratio of perspective projection may use distances between observedpositions of the object 225 (e.g., between (U₁, V₁) and (U₂, V₂)), and atime difference between observed positions of the object 225 (e.g., t₁₂)to solve for a time difference t₀₁ defined as an amount of time betweenimpact to the object 225 and a next observed position of the object 225at (U₁, V₁). Based on the time difference t₀₁, the time at impact to theobject 225 may be determined.

In some embodiments, the time difference t₀₁ may be solved for accordingto the following example expression:

d ₁₂*(d ₀₁ +d ₁₂ d ₂₃)/[(d ₀₁ +d ₁₂)*(d ₁₂ *d ₂₃)]t ₁₂*(t ₀₁ +t ₁₂ t₂₃)/[(t ₀₁ +t ₁₂)*(t ₁₂ *t ₂₃)],

in which d₀₁ may represent a distance from the starting point 235 at(U₀, V₀) to the object 225 at V₁); d₁₂ may represent a distance from(U₁, V₁) to the object 225 at (U₂, V₂); d₂₃ may represent a distancefrom (U₂, V₂) to the object 225 at a position (U₃, V₃); to′ mayrepresent an amount of time between impact to the object 225 at thestarting point 235 of (U₀, V₀) and the object 225 positioned at (U₁,V₁); 112 may represent an amount of time between the object 225positioned at (U₁, V₁) and the object 225 positioned at (U₂, V₂); t₂₃may represent an amount of time between the object 225 positioned at(U₂, V₂) and the object 225 positioned at (U₃, V₃); and “*” and “I” mayrespectively represent scalar multiplication and scalar divisionoperators. In these or other embodiments, one or more of the timedifferences such as t₁₂, t₂₃, and up to t_((n−1)n) may be determinedusing the rolling shutter method described above. Additionally oralternatively, d₁₂, d₂₃, and up to d_((n−1)n) may be the same orapproximately the same distances assuming speed of the object 225 to beconstant and the rolling shutter (e.g., frame rate, observation speed,etc.) to be constant.

FIG. 2C illustrates an example relationship between observed positionsof the object 225 in the environment 200 of FIGS. 2A-2B. Therelationship may be arranged in accordance with at least one embodimentdescribed in the present disclosure. As illustrated, the relationshipmay include the object 225 at (U₁, V₁), the object 225 at (U₂, V₂), afirst ray 240, a second ray 245, a flight vector 250, an angle theta255, and a camera 260.

In some embodiments, the flight vector 250 may be used to determine oneor more flight parameters (e.g., the flight parameters 325 of FIG. 3 ).The flight vector 250 may be determined by connecting in a straightline, the object 225 at (U₁, V₁) and the object 225 at (U₂, V₂). Thus,in some embodiments, the flight vector 250 may be a direction of travelfor the object 225 in the U-V coordinate system. In these or otherembodiments, the object 225 may be assumed to travel in a straight linebetween (U₁, V₁) and (U₂, V₂). Additionally or alternatively, theportion of the flight vector 250 between the object 225 at (U₁, V₁) andthe object 225 at (U₂, V₂) may be the same length as: another portion ofthe flight vector 250 between the object 225 at (U₂, V₂) and the object225 at (U₃, V₃); another portion of the flight vector 250 between theobject 225 at (U₃, V₃) and the object 225 at (U₄, V₄); and/or anotherportion of the flight vector 250 between the object 225 at (U_(n−1),V_(n−1)) and the object 225 at (U_(n), V_(n)). In these or otherembodiments, the relationship of the segment lengths of the flightvector 250 between positions of the object 225 may help to analyticallydetermine the angle theta 255 discussed below, for example, to determinea launch angle of the object 225. Additionally or alternatively, aportion of the flight vector 250 may lay in a two-dimensional plane. Thetwo-dimensional plane may include a camera center and the positions (U₁,V₁) and (U₂, V₂). In some embodiments, the two-dimensional plane mayhelp to determine the flight vector 225.

To transform the flight vector 250 from the U-V coordinate system to aglobal coordinate system (e.g., GPS coordinates), the angle theta 255may be determined. In some embodiments, the angle theta 255 may includean angle at which the flight vector 250 intersects the first ray 240. Inother embodiments, the angle theta 255 may include an angle at which theflight vector 250 intersects the second ray 245, or an angle at whichthe flight vector 250 intersects any other suitable ray (e.g., a thirdray) that extends from the camera 260 to an observed position of theobject 225 (e.g., (U₃, V₃)). Thus, in some embodiments, the angle theta255 may include an angular relationship between the flight vector 250and any suitable ray from the camera 260.

In some embodiments, the angle theta 255 may be determined analytically(e.g., trigonometry) or via optimization (e.g., Newton's method). As anexample of an analytical method, the following expression may help todetermine the angle theta 255:

[sin(θ−α)]÷b=[sin(α)]÷x ₁,

in which θ may represent the angle theta 255 to be determined; a mayrepresent an angle formed by the first ray 240 and the second ray 245relative to the camera 260; b may represent a distance along the firstray 240 from the camera 260 to the observed position (U₁, V₁); x₁ mayrepresent a distance along the flight vector 250 measured between theobserved positions (U₁, V₁) and (U₂, V₂); and “÷” may represent a scalardivision operator. In these or other embodiments, x₁ may be the same asor similar to die described above in conjunction with FIGS. 2A-2B.Additionally or alternatively, all variables of the above trigonometricexpression may be known or predetermined, except the angle theta 255that can be solved for using the above example expression. In otherembodiments, an example of Newton's optimization method to determine theangle theta 255 may include iteratively finding roots of adifferentiable function that approximately describes the flight vector250, the first ray 240, and the angle theta 255.

In some embodiments, after the angle theta 255 is determined, the flightvector 250 may be transformed into a transformed flight vector havingglobal coordinates by using an accelerometer associated with the camera260. For example, the accelerometer may indicate a direction of gravity,which may be used as a global coordinate axis or used as a reference toidentify one or more global coordinate axes. Thus, in some embodiments,the positional differences between a camera reference frame and theglobal coordinate system may be quantified with accelerometer data fromthe accelerometer associated with the camera 260 (e.g., +three degreesoffset). Additionally or alternatively, one or more global coordinateaxes may correspond to a ground surface, a direction parallel to asurface of the camera 260, or some other suitable reference plane ordirection. In these or other embodiments, the transformed flight vectorhaving global coordinates may be a direction of travel for the object225 in global coordinates (e.g., GPS coordinates or other suitablecoordinates).

In some embodiments, the transformed flight vector may be used todetermine one or more flight parameters, such as a launch angle and alaunch direction of the object 225. The launch angle and the launchdirection of the object 225 may be some of the flight parameters 325discussed below in conjunction with FIG. 3 . In these or otherembodiments, the launch angle may be determined by continuing along thetransformed flight vector in a direction opposite the direction oftravel of the object 225 until reaching the ground at the starting point235. The angle at which the transformed flight vector is positionedrelative to the ground may include the launch angle. In someembodiments, the launch angle may describe how the transformed flightvector is positioned in a Y-Z plane, in which +Y is vertically up and +Zis parallel to the ground and directed generally towards a target regionsuch as the target region 137 of FIG. 1 .

Additionally or alternatively, the launch direction may be determined byprojecting the transformed flight vector onto the ground. The angle atwhich the projection of the transformed flight vector is positionedrelative to a target line (e.g., the +Z axis, a zero degree linedirected to the target region 137 of FIG. 1 , etc.) may include thelaunch direction. In some embodiments, the launch direction may describehow the transformed flight vector is positioned in an X-Z plane, inwhich the X direction is parallel to a front face of the camera 260 andthe +Z is parallel to the ground and directed generally towards a targetregion such as the target region 137 of FIG. 1 .

FIG. 3 illustrates an example block diagram 300 having example inputsand outputs related to a launched object. The block diagram 300 may bearranged in accordance with at least one embodiment described in thepresent disclosure. As illustrated, a data manager 305 may obtain inputs320 to output shot data 340. In these or other embodiments, the term“obtain” may include generating, acquiring, receiving, determining,calculating, etc. The data manager may include a computing system 310and a neural network 315, which may be the same as or similar to thesystem 110 and the neural network 115 described above in conjunctionwith FIG. 1 .

In some embodiments, the inputs 320 may include, for example, flightparameters 325, mapping data 330, and club data 335. The flightparameters 325 may include the launch angle and launch directiondescribed above in conjunction with FIG. 2C. Additionally oralternatively, the flight parameters 325 may include a time differencebetween observed positions of the object as described above inconjunction with FIGS. 2A-2B. Additionally or alternatively, the flightparameters 325 may include a time of impact to the object as alsodescribed above in conjunction with FIGS. 2A-2B.

In some embodiments, another one of the flight parameters 325 mayinclude spin motion data of the object, including rate of spin anddirection of spin (e.g., spin axis). In these or other embodiments,determining spin motion of the object may include using markings on theobject. The markings may be positioned in predetermined locations on theobject, e.g., one hundred twenty degrees relative to each other andspaced equidistant to form a triangle having a center portion thatcorresponds to a center portion of the object. Other suitableconfigurations of markings may be employed. In these or otherembodiments, when the object is spinning, the markings on the object mayform a predetermined blur shape, blur line, or blur pattern based on theconfiguration of the markings. For example, in the scenario oftriangle-shaped markings, a blur line may form due to spin of the objectand an exposure duration of a camera observing the object.

Additionally or alternatively, the blur line may help to identify thespin axis and/or spin rate. For example, a 1500 RPM rotation of theobject may be predetermined as configured to induce a blur line ofapproximately nine degrees offset from a spin axis. Other RPMs of theobject may be predetermined as configured to induce a blur line of abouttwo degrees, about four degrees, about twelve degrees, about twenty fivedegrees, about fifty degrees, about ninety degrees, about one hundredtwenty degrees, etc. offset from the spin axis. Thus, in someembodiments, predetermined relationships between spin rate (e.g., RPM)and observed positioning of the spin axis may be used to approximate thespin rate of the object. Additionally or alternatively, the blur linemay of itself indicate the actual spin axis of the object. In these orother embodiments, identification of the spin axis may includeidentification of the direction of spin (e.g., clockwise orcounter-clockwise about the spin axis). For example, the manner in whichthe spin axis is positioned or shaped may indicate which direction theobject is spinning.

In some embodiments, motion blur may also be observed as a result oflinear motion of the object, and not as a result of rotational motion orspin. However, the motion blur and rotational blur may fuse togetherwhen observed, appearing inseparable or indistinguishable. In these orother embodiments, the motion blur and the rotational blur may beseparated to help increase accuracy of one or more of the flightparameters 325 such as the spin rate and the spin axis. Separating thetwo different kinds of blurs may be done in myriad different ways. Inone example way, a point spread function may be used in deconvolution ofblur. Sample equations and examples of some point spread functions usedin deconvolution processes relating to motion blur are described inadditional detail in the paper entitled IMAGE MOTION DEBLURRING byDaniel Cunningham archived athttp://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/AV0506/s0198594.pdf(hereafter “Cunningham Paper”). The contents of the Cunningham Paper inits entirety are expressly incorporated by reference into the presentdisclosure.

In some embodiments, the flight parameters 325 may include a speed ofthe object. For example, a radar sensor may generate radar data of theobject after the object is launched. Additionally or alternatively, theradar data may be analyzed to ascertain a measured speed of the object.Analysis of the radar data may include myriad different ways ofanalysis. In one example way of analysis, a Fast Fourier transform (FFT)may be performed on the radar data, e.g., by the computing system 310.In these or other embodiments, the analysis may be performed on theradar data corresponding to a time around launch of the object (e.g.,around time t₀₁ described above in conjunction with FIGS. 2A-2B). In anexample analysis including an FFT, the FFT may include a Cooley-TukeyFFT, a Prime-factor FFT, a Bruun's FFT, a Rader's FFT, a Bluestein'sFFT, a Goertzel algorithm, a Radix algorithm, a Fast Hartley Transform,a Quick Fourier Transform, a Decimation-in-Time-Frequency algorithm, acombination thereof, or any other suitable algorithm. Sample equationsand examples of some types of FFT analysis are described in additionaldetail in the paper entitled FFT ALGORITHMS by Brian Gough dated May1997 and archived at https://pdfs.semanticscholar.org/ca1c/a09567ef927c6b545 af435P96e9e49ba43ee.pdf (hereafter the “GoughPaper”). The contents of the Gough Paper in its entirety are expresslyincorporated by reference into the present disclosure.

In these or other embodiments, the analysis on the radar data may outputa measured speed of the object. The measured speed of the object, insome cases, may include a fraction of the actual speed of the objectsince the radar sensor may measure the portion of the speed of theobject in the radial direction of the radar sensor, not the actual speedof the object along its trajectory. Thus, in some embodiments, theactual speed of the object may be found by dividing the measured speedof the object by the cosine of the angle theta 255 described above inconjunction with FIG. 2C. The actual speed of the object may be one ofthe flight parameters 325.

In some embodiments, the mapping data 330 may include a satellite imageor a flyby image. The satellite image or flyby image may be obtained viaone or more service providers such as Google® Maps, Google® Earth,Apple® Maps, etc. Additionally or alternatively, the mapping data 330may include GPS coordinates or other suitable positioning data. The GPScoordinates may correspond to a client device, a sensor (such as one ofthe sensors 220 of FIG. 2 ), a measuring device, or any other suitabledevice. In these or other embodiments, the GPS coordinates may beassociated with a location of the object, for example, due to proximityto the object. Additionally or alternatively, the GPS coordinates maycorrespond to the object itself, for example, due to a GPS chip on orwithin the object that is configured to relay GPS data to the computingsystem 310 and/or permit acquisition of GPS data by the computing system310 (e.g., via a wireless connection such as a BlueTooth® connection).

In some embodiments, the club data 335 may include motion data. Themotion data may include radar data, motion sensor data, and/or othersuitable data configured to indicate motion. In these or otherembodiments, the motion data may trigger a club recognition mode (e.g.,of the computing system 310). For example, upon receiving the motiondata at the computing system 310, the computing system 310 may cause acamera communicatively coupled thereto to obtain additional club data335 in the form of one or more images. Additionally or alternatively,upon receiving the motion data at the computing system 310, thecomputing system 310 may analyze one or more images in an image buffer.The image buffer may be configured as data storage (e.g., as part of thecomputing system 310) that may receive data on a rolling basis and/ormay delete data on a rolling basis as the image buffer satisfies a datathreshold.

As described above, the inputs 320 may include the flight parameters325, the mapping data 330, and the club data 335. The data manager 305may process one or more of the inputs 320 to output one or more aspectsof the shot data 340. In some embodiments, the shot data 340 may includea simulated trajectory 345, flight mapping 350, media content 355, andclub identification 360.

In some embodiments, the simulated trajectory 345 may be the same as orsimilar to the trajectory 130 described above in conjunction with FIG. 1. The simulated trajectory 345 may be determined by the data manager 305in myriad different ways. In one example way, an ordinary differentialequation (ODE) may be used to determine the simulated trajectory 345.For example, one or more of the flight parameters 325 may be input intothe ODE to output the simulated trajectory 345, the expression of whichis as follows: simulated trajectory 345=f(flight parameters 325), wherethe term f( ) may represent the ODE. In some embodiments, solving theODE may include iterative solutions that may represent positions of theobject at example time steps of Δt. In psuedo-equation form of anexample ODE, a first position of the object may be determined.Afterwards, a second position of the object at time Δt later may bedetermined, by multiplying the speed of the object at the previousposition (e.g., the first position) by time Δt. Additionally oralternatively, the speed of the object at any given position may beupdated. For example, the speed of the object at the second position maybe determined by multiplying adjusted spin data of the object by timeΔt. In these or other embodiments, the adjusted spin data may be basedon spin data of the previous position (e.g., the first position). Thus,in some embodiments, the computing system 310 may iteratively executethe ODE to determine positions of the object at example time steps of Δtto determine the simulated trajectory 345.

Additionally or alternatively, any of the flight parameters 325 may bedetermined for a first time and/or updated as the simulated trajectory345 is modeled. For example, not all of the flight parameters 325 may beused as an input 320. Rather, some of the flight parameters 325 may beconsidered as outputs and may be determined as the simulated trajectory345 is modeled. For example, one or more of the flight parameters 325may be determined using an ordinary differential equation (ODE). In someembodiments, solving the ODE may include iterative solutions that mayrepresent spin rate and/or speed of the object at example time steps ofΔt. In psuedo-equation form of an example ODE, a first position of theobject may be obtained. Afterwards, a second position of the object attime Δt later may be obtained. Given a time step of time Δt and thepositional difference between the first position and the secondposition, the speed of the object may be determined. Additionally oralternatively, the speed of the object at any given position may beupdated. For example, the speed of the object at the second position maybe determined by multiplying adjusted spin data of the object by timeΔt. In these or other embodiments, the adjusted spin data may be basedon spin data of the previous position (e.g., the first position) or anyother flight parameter 325. Thus, in some embodiments, the computingsystem 310 may iteratively execute the ODE to determine and/or updateflight parameters 325 like speed and spin rate of the object at exampletime steps of Δt in the simulated trajectory 345 described furtherbelow.

In some embodiments, the simulated trajectory 345 may be in global,three-dimensional coordinates such as GPS coordinates after beingdetermined by the data manager 305. To output the simulated trajectory345 on a display, the simulated trajectory 345 may be transformed forpresentation at a two-dimensional image plane of the display. An exampleof the display may include a graphical user interface communicativelycoupled to the computing system 310. In these or other embodiments,converting the simulated trajectory 345 may include use of a perspectiveprojection matrix such as a 3×4 matrix (hereafter PPM).

In some embodiments, The PPM may include a K-matrix multiplied by anRT-matrix. The K-matrix may include a 3×3 matrix that includes intrinsicparameters of a camera communicatively coupled to the computing system310. Examples of intrinsic parameters may include image planecoordinates (e.g., U-V coordinates) of the camera reference frame, pixelcoordinates, and any other suitable parameters that may be used to linkpixel coordinates of an image point with corresponding coordinates in acamera reference frame. The RT-matrix may include the combination of anR-matrix (e.g., a 3×3 rotational matrix), and a T-matrix that is aunitary matrix, such as a 3×1 zero matrix. Thus, in some embodiments,the RT-matrix may include a 3×4 matrix. In these or other embodiments,an example result of multiplying a matrix, such as the K-matrix, by theRT-matrix is transformation of the camera coordinate frame to a globalcoordinate frame.

In some embodiments, the PPM may be multiplied by the simulatedtrajectory 345, which may be represented by a 4Xn matrix, in which theterm “n” is the number of points in the simulated trajectory 345. Theresulting cross product of the PPM and the simulated trajectory 345 mayinclude a converted version of the simulated trajectory 345 described inhomogeneous coordinates. The homogeneous coordinates may help toaccurately represent three-dimensional information in a two-dimensionalimage plane of the display, at which a version of the simulatedtrajectory 345 may be presented.

In some embodiments, error may be introduced when converting thesimulated trajectory 345 into the converted version of the simulatedtrajectory 345. The error may be described as a cost function in whichdifferences between the simulated trajectory 345 and the convertedversion of the simulated trajectory 345 may be given quantitativevalues. In these or other embodiments, the cost function may beoptimized to help provide an improved or more accurate converted versionof the simulated trajectory 345. Additionally or alternatively,optimization of the cost function may include assuming one or more ofthe flight parameters 325 as constant or fixed over a given period oftime, e.g., for reducing error. As used herein, the term “optimize”should be interpreted to mean “improved,” “enhanced” or “local optima,”and not necessarily as “absolute optima,” “true optimization” or the“best,” although an “absolute optima” or “best” may still be covered bythe present disclosure. For example, an optimization process may improveupon a solution that was there previously, may find the best solution,or may verify that an existing solution is a “local optima” or an“absolute optima” and thus should not be modified or changed.

In some embodiments, the flight mapping 350 may include the satelliteimage or flyby image of the environment onto which the simulatedtrajectory 345 may be superimposed. Additionally or alternatively, theflight mapping 350 may include a target line superimposed onto thesatellite image or flyby image of the environment. The target line maybe generated based on compass data (e.g., of a client device pointedgenerally in the direction of the launch of the object). In these orother embodiments, the compass data may not perfectly align the objectat rest (pre-launch) with a desired target region at which a playerintends to place the object. In these or other embodiments, the targetregion may be selected by the user via a user interface (e.g., of thecomputing system 310 of a client device or other suitable device). Thetarget region may be associated with GPS coordinates, and based on thetarget region coordinates, the target line may be adjusted. For example,given a set of GPS coordinates for a particular target region, a clientdevice may initially provide compass data that is offset from the targetregion. Then the computing system 310 may cause the client device toautomatically update the target line to align with the actual positionof the target region based on the associated GPS coordinates. In theseor other embodiments, the flight mapping 350 may be presented at adisplay, for example, along with media content 355 described below.

The media content 355 may include one or more of: an image, a video,replay content, previous player content, multi-player content, singleplayer content, interactive content, entertainment, betting or gamblingcontent, competition content, shot statistics, position data, targetregion data, environment data, the simulated trajectory 345, the flightmapping 350, and the club identification 360 described below. In theseor other embodiments, the media content 355 may be presented at adisplay of the computing system 310. The display may be part of orcommunicatively coupled to a client device, a third-party device, ameasuring device, a device proximate to an object launch area, anetwork-connected device, or any other suitable device.

The club identification 360 may include an identified club and anyassociated data related thereto. In these or other embodiments, the clubidentification 360 may be determined using image processing and imagerecognition techniques. In an example scenario, upon receiving the clubdata 335 at the computing system 310, the computing system 310 may causea camera communicatively coupled thereto to obtain additional club data335 in the form of one or more images. Additionally or alternatively,using an object detection network such as, for example, a Single ShotDetector (SSD), the club and an associated club identifier may belocated. The object detection network may, using one or more of theimages, determine a type of club (e.g., wood, iron, hybrid).Additionally or alternatively, one or more images of the club may beprovided to the neural network 315. The neural network 315 may, usingtechniques described in conjunction with the neural network 115 of FIG.1 , identify the type of club and/or the club identifier. The clubidentifier may include a number or name, a shape, an outline, amaterial, an amount of reflectivity, a shaft, a face angle, a headmeasurement, etc. that may help the data manager 305 to identify theclub and output the club identification 360.

FIG. 4 illustrates an example method 400 to simulate a trajectory of anobject. The method 400 may be performed according to one or moreembodiments described in the present disclosure. In these or otherembodiments, the method 400 may be performed by processing logic thatmay include hardware (circuitry, dedicated logic, etc.), software (suchas is run on a computer system), or a combination of both, whichprocessing logic may be included in a client device, or another computersystem or device. However, another system, or combination of systems,may be used to perform the method 400. For simplicity of explanation,methods described herein are depicted and described as a series of acts.However, acts in accordance with this disclosure may occur in variousorders and/or concurrently, and with other acts not presented anddescribed herein. Further, not all illustrated acts may be used toimplement the methods in accordance with the disclosed subject matter.In addition, those skilled in the art will understand and appreciatethat the methods may alternatively be represented as a series ofinterrelated states via a state diagram or events. Additionally, themethods disclosed in this specification are capable of being stored onan article of manufacture, such as a non-transitory computer-readablemedium, to facilitate transporting and transferring such methods tocomputing devices. The term article of manufacture, as used herein, isintended to encompass a computer program accessible from anycomputer-readable device or storage media. Although illustrated asdiscrete blocks, various blocks may be divided into additional blocks,combined into fewer blocks, or eliminated, depending on the desiredimplementation.

The method 400 may begin at block 405, where a group of images taken bya camera over time in an environment may be received. In someembodiments, the camera may be oriented within the environment tocapture images in a substantially same direction as a launch directionof the object. At block 410, a first position of the object in the firstimage may be identified. At block 415, a second position of the objectin the second image may be identified. In some embodiments, motion dataof the object received from a motion sensor or radar sensor may triggerthe identifying of the object in buffered images of the first image andthe second image. At block 420, a flight vector based on the firstposition of the object and the second position of the object may begenerated. In some embodiments, the flight vector may include aconnecting line between the object in the first position and the objectin the second position.

At block 425, one or more flight parameters may be determined using theflight vector. In some embodiments, determining one or more flightparameters using the flight vector may include determining a launchangle and the launch direction of the object by: generating a first rayfrom the camera to the object in the first image; generating a secondray from the camera to the object in the second image; generating theflight vector by creating a line intersecting both the first ray and thesecond ray at the object in the first image and the second image,respectively; and/or determining a transformed flight vector relative tothe camera to be relative to a Global Positioning System (GPS)coordinate system by determining an angle theta at which the flightvector intersects the first ray, the transformed flight vector being adirection of travel for the object. In some embodiments, the one or moreflight parameters include an actual speed of the object. Additionally oralternatively, a radar speed of the object may be determined using aFast Fourier Transform of radar data of the object. Additionally oralternatively, the actual speed of the object may be determined bydividing the radar speed of the object by a cosine of the angle theta.

In some embodiments, at block 425, one or more additional flightparameters based on rotational blur of the object in the first andsecond images, including a spin rate and a spin axis for the object, maybe determined. In these or other embodiments, the object may includepredetermined markings arranged such that the spin rate and the spinaxis of the object are determined based on a manner in which thepredetermined markings are captured by the camera in the first image andthe second image due to the rotational blur of the object in the firstimage and the second image.

At block 430, a simulated trajectory of the object may be generatedbased on the flight parameters determined at block 425. In someembodiments, as the simulated trajectory of the object is generated, oneor more flight parameters not determined at block 425 may be determined.At block 435, the simulated trajectory of the object may be provided forpresentation in a graphical user interface of a display. In someembodiments, providing the simulated trajectory of the object forpresentation in the graphical user interface may include: converting thesimulated trajectory into a two-dimensional image plane; superimposingthe converted simulated trajectory onto a satellite image or a flybyimage of the environment; and/or presenting a video of the object beinglaunched and flight data of the object.

One skilled in the art will appreciate that, for these processes,operations, and methods, the functions and/or operations performed maybe implemented in differing order. Furthermore, the outlined functionsand operations are only provided as examples, and some of the functionsand operations may be optional, combined into fewer functions andoperations, or expanded into additional functions and operations withoutdetracting from the essence of the disclosed embodiments. For example,another block in the method 400 may include determining a first momentin time associated with the object in the first image and a secondmoment in time associated with the object in the second image using arolling shutter of the camera. In another example, a block in the method400 may include determining an impact time at which the object isimpacted based on the first moment in time and the first positionassociated with the object in the first image and based on the secondmoment in time and the second position associated with the object in thesecond image. In another example, a block in the method 400 may includeautomatically identifying a club associated with the simulatedtrajectory of the object using a neural network to identify a clubidentifier of the club captured in one or more images of the pluralityof images.

FIG. 5 illustrates an example system 500 that may be used to simulate atrajectory of an object. The system 500 may be arranged in accordancewith at least one embodiment described in the present disclosure. Thesystem 500 may include a processor 510, memory 512, a communication unit516, a display 518, a user interface unit 520, and a peripheral device522, which all may be communicatively coupled. In some embodiments, thesystem 500 may be part of any of the systems or devices described inthis disclosure. For example, the system 500 may be part of the datamanager 105 of FIG. 1 . Additionally or alternatively, the system 500may be part of the computing system 110 and/or the neural network 115.

Generally, the processor 510 may include any computer, computing entity,or processing device including various computer hardware or softwaremodules and may be configured to execute instructions stored on anyapplicable computer-readable storage media. For example, the processor510 may include a microprocessor, a microcontroller, a digital signalprocessor (DSP), an application-specific integrated circuit (ASIC), aField-Programmable Gate Array (FPGA), or any other digital or analogcircuitry configured to interpret and/or to execute program instructionsand/or to process data.

Although illustrated as a single processor in FIG. 5 , it is understoodthat the processor 510 may include any number of processors distributedacross any number of networks or physical locations that are configuredto perform individually or collectively any number of operationsdescribed in this disclosure. In some embodiments, the processor 510 mayinterpret and/or execute program instructions and/or process data storedin the memory 512. In some embodiments, the processor 510 may executethe program instructions stored in the memory 512.

For example, in some embodiments, the processor 510 may execute programinstructions stored in the memory 512 that are related to, for example,simulating a trajectory of an object such that the system 500 mayperform or direct the performance of the operations associated therewithas directed by the instructions. In these and other embodiments,instructions may be used to perform one or more operations of the method400 of FIG. 4 described above.

The memory 512 may include computer-readable storage media or one ormore computer-readable storage mediums for carrying or havingcomputer-executable instructions or data structures stored thereon. Suchcomputer-readable storage media may be any available media that may beaccessed by a computer, such as the processor 510. By way of example,and not limitation, such computer-readable storage media may includenon-transitory computer-readable storage media including Random AccessMemory (RAM), Read-Only Memory (ROM), Electrically Erasable ProgrammableRead-Only Memory (EEPROM), Compact Disc Read-Only Memory (CD-ROM) orother optical disk storage, magnetic disk storage or other magneticstorage devices, flash memory devices (e.g., solid state memorydevices), or any other storage medium which may be used to carry orstore particular program code in the form of computer-executableinstructions or data structures and which may be accessed by a computer.Combinations of the above may also be included within the scope ofcomputer-readable storage media. Computer-executable instructions mayinclude, for example, instructions and data configured to cause theprocessor 510 to perform a certain operation or group of operations asdescribed in this disclosure. In these and other embodiments, the term“non-transitory” as explained in the present disclosure should beconstrued to exclude only those types of transitory media that werefound to fall outside the scope of patentable subject matter in theFederal Circuit decision of In re Nuijten, 500 F.3d 1346 (Fed. Cir.2007). Combinations of the above may also be included within the scopeof computer-readable media.

The communication unit 516 may include any component, device, system, orcombination thereof that is configured to transmit or receiveinformation over a network. In some embodiments, the communication unit516 may communicate with other devices at other locations, the samelocation, or even other components within the same system. For example,the communication unit 516 may include a modem, a network card (wirelessor wired), an infrared communication device, a wireless communicationdevice (such as an antenna), and/or chipset (such as a Bluetooth device,an 802.6 device (e.g., Metropolitan Area Network (MAN)), a Wi-Fi device,a WiMax device, cellular communication facilities, etc.), and/or thelike. The communication unit 516 may permit data to be exchanged with anetwork and/or any other devices or systems described in the presentdisclosure.

The display 518 may be configured as one or more displays, like an LCD,LED, a monitor, a screen, or other type of display. The display 518 maybe configured to output shot data such as a simulated trajectory of anobject, flight mapping, media content, and club identification; aschematic or representation of an environment in which the objecttraveled at some point during the trajectory, user interfaces, and otherdata as directed by the processor 510.

The user interface unit 520 may include any device to allow a user tointerface with the system 500. For example, the user interface unit 520may include a mouse, a track pad, a keyboard, buttons, and/or atouchscreen, among other devices. The user interface unit 520 mayreceive input from a user and provide the input to the processor 510. Insome embodiments, the user interface unit 520 and the display 518 may becombined. For example, a player may select, via the user interface unit520, a target region at which placement of the object is desired.

The peripheral devices 522 may include one or more devices. For example,the peripheral devices may include a sensor, a microphone, and/or aspeaker, among other peripheral devices. In these and other embodiments,the microphone may be configured to capture audio. The speaker maybroadcast audio received by the system 500 or otherwise generated by thesystem 500. The sensor may be configured to sense changes in in light,pixels, sound, motion, rotation, position, orientation, magnetization,acceleration, tilt, vibration, force, speed, color, wind, etc.

Modifications, additions, or omissions may be made to the system 500without departing from the scope of the present disclosure. For example,in some embodiments, the system 500 may include any number of othercomponents that may not be explicitly illustrated or described. Further,depending on certain implementations, the system 500 may not include oneor more of the components illustrated and described.

As indicated above, the embodiments described herein may include the useof a computer (e.g., a processor element) including various computerhardware or software modules. Further, as indicated above, embodimentsdescribed herein may be implemented using computer-readable media (e.g.,a memory element) for carrying or having computer-executableinstructions or data structures stored thereon.

In accordance with common practice, the various features illustrated inthe drawings may not be drawn to scale. The illustrations presented inthe present disclosure are not meant to be actual views of anyparticular apparatus (e.g., device, system, etc.) or method, but aremerely idealized representations that are employed to describe variousembodiments of the disclosure. Accordingly, the dimensions of thevarious features may be arbitrarily expanded or reduced for clarity. Inaddition, some of the drawings may be simplified for clarity. Thus, thedrawings may not depict all of the components of a given apparatus(e.g., device) or all operations of a particular method.

Terms used herein and especially in the appended claims (e.g., bodies ofthe appended claims) are generally intended as “open” terms (e.g., theterm “including” should be interpreted as “including, but not limitedto,” the term “having” should be interpreted as “having at least,” theterm “includes” should be interpreted as “includes, but is not limitedto,” etc.).

Additionally, if a specific number of an introduced claim recitation isintended, such an intent will be explicitly recited in the claim, and inthe absence of such recitation no such intent is present. For example,as an aid to understanding, the following appended claims may containusage of the introductory phrases “at least one” and “one or more” tointroduce claim recitations. However, the use of such phrases should notbe construed to imply that the introduction of a claim recitation by theindefinite articles “a” or “an” limits any particular claim containingsuch introduced claim recitation to embodiments containing only one suchrecitation, even when the same claim includes the introductory phrases“one or more” or “at least one” and indefinite articles such as “a” or“an” (e.g., “a” and/or “an” should be interpreted to mean “at least one”or “one or more”); the same holds true for the use of definite articlesused to introduce claim recitations.

In addition, even if a specific number of an introduced claim recitationis explicitly recited, those skilled in the art will recognize that suchrecitation should be interpreted to mean at least the recited number(e.g., the bare recitation of “two recitations,” without othermodifiers, means at least two recitations, or two or more recitations).Furthermore, in those instances where a convention analogous to “atleast one of A, B, and C, etc.” or “one or more of A, B, and C, etc.” isused, in general such a construction is intended to include A alone, Balone, C alone, A and B together, A and C together, B and C together, orA, B, and C together, etc. For example, the use of the term “and/or” isintended to be construed in this manner.

Further, any disjunctive word or phrase presenting two or morealternative terms, whether in the description, claims, or drawings,should be understood to contemplate the possibilities of including oneof the terms, either of the terms, or both terms. For example, thephrase “A or B” should be understood to include the possibilities of “A”or “B” or “A and B.”

However, the use of such phrases should not be construed to imply thatthe introduction of a claim recitation by the indefinite articles “a” or“an” limits any particular claim containing such introduced claimrecitation to embodiments containing only one such recitation, even whenthe same claim includes the introductory phrases “one or more” or “atleast one” and indefinite articles such as “a” or “an” (e.g., “a” and/or“an” should be interpreted to mean “at least one” or “one or more”); thesame holds true for the use of definite articles used to introduce claimrecitations.

Additionally, the use of the terms “first,” “second,” “third,” etc., arenot necessarily used herein to connote a specific order or number ofelements. Generally, the terms “first,” “second,” “third,” etc., areused to distinguish between different elements as generic identifiers.Absence a showing that the terms “first,” “second,” “third,” etc.,connote a specific order, these terms should not be understood toconnote a specific order. Furthermore, absence a showing that the terms“first,” “second,” “third,” etc., connote a specific number of elements,these terms should not be understood to connote a specific number ofelements. For example, a first widget may be described as having a firstside and a second widget may be described as having a second side. Theuse of the term “second side” with respect to the second widget may beto distinguish such side of the second widget from the “first side” ofthe first widget and not to connote that the second widget has twosides.

All examples and conditional language recited herein are intended forpedagogical objects to aid the reader in understanding the invention andthe concepts contributed by the inventor to furthering the art, and areto be construed as being without limitation to such specifically recitedexamples and conditions. Although embodiments of the present disclosurehave been described in detail, it should be understood that the variouschanges, substitutions, and alterations could be made hereto withoutdeparting from the spirit and scope of the present disclosure.

What is claimed is:
 1. A method, comprising: receiving a plurality ofimages taken by a camera over time in an environment, the camera beingoriented within the environment to capture images of an object in asubstantially same direction as a launch direction of the object;generating a flight vector based on a first position of the object and asecond position of the object, the first position and second positionhaving been determined using the plurality of images; determining one ormore flight parameters using the flight vector; generating a simulatedtrajectory of the object based on the flight parameters; and providingthe simulated trajectory of the object for presentation in a graphicaluser interface.